A protein-coding gene expression atlas from the brain of pregnant and non-pregnant goats

Background: The brain is an extraordinarily complex organ with multiple anatomical structures involved in highly specialized functions related with behavior and physiological homeostasis. Our goal was to build an atlas of protein-coding gene expression in the goat brain by sequencing the transcriptomes of 12 brain regions in seven female Murciano-Granadina goats, from which three of them were 1-month pregnant. Results: Between 14,889 (cerebellar hemisphere) and 15,592 (pineal gland) protein-coding genes were expressed in goat brain regions, and most of them displayed ubiquitous or broad patterns of expression across tissues. Principal component analysis and hierarchical clustering based on the patterns of mRNA expression revealed that samples from certain brain regions tend to group according to their position in the anterior-posterior axis of the neural tube, i.e., hindbrain (pons and medulla oblongata), midbrain (rostral colliculus) and forebrain (frontal neocortex, olfactory bulb, hypothalamus, and hippocampus). Exceptions to this observation were cerebellum and glandular tissues (pineal gland and hypophysis), which showed highly divergent mRNA expression profiles. Differential expression analysis between pregnant and non-pregnant goats revealed moderate changes of mRNA expression in the frontal neocortex, hippocampus, adenohypophysis and pons, and very dramatic changes in the olfactory bulb. Many genes showing differential expression in this organ are related to olfactory function and behavior in humans. Conclusion: With the exception of cerebellum and glandular tissues, there is a relationship between the cellular origin of sampled regions along the anterior-posterior axis of the neural tube and their mRNA expression patterns in the goat adult brain. Gestation induces substantial changes in the mRNA expression of the olfactory bulb, a finding consistent with the key role of this anatomical structure on the development of maternal behavior.

Systematic review of transcriptome and microRNAome associations with gestational diabetes mellitus

PURPOSE

Gestational diabetes (GDM) is associated with increased risk for preterm birth and related complications for both the pregnant person and newborn. Changes in gene expression have the potential to characterize complex interactions between genetic and behavioral/environmental risk factors for GDM. Our goal was to summarize the state of the science about changes in gene expression and GDM.

DESIGN

The systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

METHODS

PubMed articles about humans, in English, from any date were included if they described mRNA transcriptome or microRNA findings from blood samples in adults with GDM compared with adults without GDM.

RESULTS

Sixteen articles were found representing 1355 adults (n=674 with GDM, n=681 controls) from 12 countries. Three studies reported transcriptome results and thirteen reported microRNA findings. Identified pathways described various aspects of diabetes pathogenesis, including glucose and insulin signaling, regulation, and transport; natural killer cell mediated cytotoxicity; and fatty acid biosynthesis and metabolism. Studies described 135 unique miRNAs that were associated with GDM, of which eight (miR-16-5p, miR-17-5p, miR-20a-5p, miR-29a-3p, miR-195-5p, miR-222-3p, miR-210-3p, and miR-342-3p) were described in 2 or more studies. Findings suggest that miRNA levels vary based on the time in pregnancy when GDM develops, the time point at which they were measured, sex assigned at birth of the offspring, and both the pre-pregnancy and gestational body mass index of the pregnant person.

CONCLUSIONS

The mRNA, miRNA, gene targets, and pathways identified in this review contribute to our understanding of GDM pathogenesis; however, further research is warranted to validate previous findings. In particular, longitudinal repeated-measures designs are needed that control for participant characteristics (e.g., weight), use standardized data collection methods and analysis tools, and are sufficiently powered to detect differences between subgroups. Findings may be used to improve early diagnosis, prevention, medication choice and/or clinical treatment of patients with GDM.